Advances in hypofractionated radiotherapy techniques have shown promise in the treatment of cancers that are conventionally associated with high morbidity and poor local control (e.g. lung and liver cancer). The small number of high dose treatment fractions requires superior precision and accuracy in target delineation, conformal treatment planning, and target localization at the time of treatment. Advances in imaging for target identification, volumetric imaging capabilities at treatment, and temporal imaging technologies, increase the capability of identifying the tumor during simulation, planning, and delivery. The spatial registration of this information, which is critical to correlate the unique information from each image, is limited by the lacking ability to integrate all available information into one comprehensive model of the patient. Early experience with dynamic multi-organ anatomical models for deformable registration has lead to the hypothesis that deformation technologies will improve the quality of treatment and lead to clinically significant improvements in tumor control and reduced toxicity. While testing this hypothesis will require a comprehensive program of multi-institution clinical trials, these methods need to be established and evaluated prior to deployment in clinical studies. This proposal sets out three specific aims to assure that the technologies are ready for translation into the clinical context, specifically in the lung, liver, and pancreas. In specific aim 1, dynamic multi-organ anatomical models will be developed and validated for the lung, liver, and pancreas. The accuracy of these models and linear interpolation between breathing states will be quantified. Heterogeneous material models will be optimized for the lung and liver. The influence of multi-organ deformable registration on the design and targeting of hypofractionated radiotherapy will be investigated in specific aim 2. The increase in accuracy of multi-modality treatment planning with deformable registration will be evaluated. The improvements in dosimetric accuracy with the inclusion of motion and deformation due to breathing will be quantified. The translation of this increase in accuracy into clinical dose effect models will be investigated. Specific aim 3 evaluates the impact of deformable registration on documentation and accounting of dose in hypofractionated radiotherapy. The improvements in accuracy of image guidance using deformable registration will be assessed. The increase in accuracy of the documentation of accumulated dose over treatment will be investigated, as well as the translation of this improvement in dose effect models. The goal of this research is to improve the accuracy and reduce the uncertainty in radiation therapy. Through the use of dynamic multi-organ anatomical models the wealth of information obtained from advanced imaging techniques will be combined into one, clear, model of the patient. This enhanced patient model will allow improved accuracy in the design and implementation of treatment.